Search Results (406)

Background and Objectives: Chronic lung diseases act as multi-organ conditions in which systemic inflammation, vascular dysfunction, and fibrosis intersect. The pulmo-renal continuum—functional crosstalk between lungs and kidneys—remains poorly characterized. We compared year-long changes in endothelin-1 (ET-1), galectin-3 (Gal-3), renal indices (eGFR, ACR), and quantitative CT densitometry in COPD and systemic sclerosis-associated ILD (SSc-ILD). Materials and Methods: In this prospective observational study (January 2023–December 2024), 112 patients were consecutively enrolled (COPD, n = 58; SSc-ILD, n = 54). Assessments were performed at baseline and 12 months. ET-1 (ELISA) and Gal-3 (chemiluminescence) were measured in serum; eGFR was calculated by the creatinine-based CKD-EPI (2021) equation; ACR was photometric. High-resolution chest CT provided lung volume and parenchymal density (Hounsfield units) at six predefined axial levels per lung. Non-parametric statistics were applied: Wilcoxon signed-rank (within-group), Mann–Whitney U (between-group), and Spearman rank correlations for associations; results are reported with p-values (and 95% CIs). Results: Baseline eGFR was normal (COPD 90.37; SSc-ILD 92.4 mL/min/1.73 m²). eGFR declined by 6.76% in COPD (p = 0.001) and 3.16% in SSc-ILD (p = 0.029). ET-1 increased in both cohorts but more in COPD (+83.78%, p = 0.0002) than in SSc-ILD (+23.83%, p = 0.0001). Gal-3 rose significantly only in SSc-ILD (+10.2%, p = 0.043). FVC decreased in COPD (−4.01%, p = 0.01) and was unchanged in SSc-ILD. Total lung volume declined in SSc-ILD (−6.08%, p = 0.02) but not in COPD. CT density shifts were small: several slices in COPD and one slice (L6) in SSc-ILD reached statistical but not biological relevance. Conclusions: COPD exhibited larger vascular and renal biomarker shifts (ET-1 up, eGFR down, ACR up), suggesting systemic inflammation and early renal involvement. In SSc-ILD, biomarker and CT changes predominantly reflected pulmonary fibrosis progression with limited renal impact. Integrating biomarkers with quantitative CT may help delineate organ-specific trajectories along the pulmo-renal continuum; longer, larger studies are warranted. Limitations: This was a single-center cohort with a modest sample (58 COPD and 54 SSc-ILD) and a 12-month, two-time-point follow-up, which may not capture long-term trajectories and may limit it generalizability; larger multicenter studies with an extended follow-up are warranted. Full article

Background/Objectives: Gout, a complex metabolic disorder of increasing global incidence, remains incompletely understood in its pathogenesis. Current diagnostic approaches exhibit significant limitations, including insufficient specificity and the requirement for invasive joint aspiration, highlighting the need for non-invasive, sensitive biomarkers for early detection. Methods: Urine metabolites were extracted from 28 healthy controls, 13 asymptomatic hyperuricemia (HUA) patients, and 29 acute gouty arthritis (AGA) patients. The extracted metabolites were analyzed by UHPLC-MS/MS for untargeted metabolomics. Differential metabolites were screened by partial least squares discriminant analysis (PLS-DA) and volcano plot analysis. Pathway analysis determined the core disorder pathway of gout progression. Results: A total of 278 differential metabolites associated with gout progression were identified. The most pronounced metabolic alterations were observed between the AGA and control groups, indicative of substantial metabolic reprogramming during disease transition. Metabolic pathway analysis revealed four significantly dysregulated pathways: histidine metabolism, nicotinate and nicotinamide metabolism, phenylalanine metabolism, and tyrosine metabolism. Receiver operating characteristic (ROC) curve analysis revealed that three urine markers with high diagnostic efficacy—oxoamide, 3-methylindole, and palmitic acid—exhibited progressive alterations across the disease continuum. Conclusions: This metabolomics study identified core regulatory metabolites and newly discovered metabolic pathways underlying gout pathogenesis, along with novel urinary biomarkers capable of predicting HUA-to-AGA progression. The aberrant levels of key metabolites in the disordered pathway implicate neuroimmune dysregulation, energy metabolism disruption, and oxidative stress in gout pathogenesis. These findings provide new foundations and strategies for the daily monitoring and prevention of gout. Full article

Current IoT systems are structured around Edge, Fog, and Cloud layers to manage data and resource constraints more effectively. Although several studies have examined IoT simulators from a functional angle, few have combined technical comparisons with experimental validation under realistic conditions. This lack of integration limits the practical value of prior results and complicates tool selection for distributed architectures. This work introduces a selection and evaluation methodology for simulators that explicitly represent the Edge–Fog–Cloud continuum. Thirteen open-source tools are analyzed based on functional, technical, and operational features. Among them, iFogSim2 and FogNetSim++ are selected for a detailed experimental comparison on their support of mobility, resource allocation, and energy modeling across all layers. A shared hybrid IoT scenario is simulated using eight key metrics: execution time, application loop delay, CPU processing time per tuple, energy consumption, cloud execution cost, network usage, scalability, and robustness. The analysis reveals distinct modeling strategies: FogNetSim++ reduces loop latency by 48% and maintains stable performance at scale but shows high data loss under overload. In contrast, iFogSim2 consumes up to 80% less energy and preserves message continuity in stressful conditions, albeit with longer execution times. These outcomes reflect the trade-offs between modeling granularity, performance stability, and system resilience. Full article

Whether forensic disciplines have established foundational validity—sufficient empirical evidence that a method reliably produces a predictable level of performance—has become a question of growing interest among scientists and legal professionals. This paper evaluates the foundational validity of two sources of forensic evidence relied upon in criminal cases: eyewitness identification decisions and latent fingerprint examiners’ conclusions. Importantly, establishing foundational validity and estimating accuracy are conceptually and functionally different. Though eyewitnesses can often be mistaken, identification procedures recommended by researchers are grounded in decades of programmatic research that justifies the use of methods that improve the reliability of eyewitness decisions. In contrast, latent print research suggests that expert examiners can be very accurate, but foundational validity in this field is limited by an overreliance on a handful of black-box studies, the dismissal of smaller-scale, yet high-quality, research, and a tendency to treat foundational validity as a fixed destination rather than a continuum. Critically, the lack of a standardized method means that any estimates of examiner performance are not tied to any specific approach to latent print examination. Despite promising early work, until the field adopts and tests well-defined procedures, foundational validity in latent print examination will remain a goal still to be achieved. Full article

Luminous, hot, massive stars can lose mass both through quasi-steady winds driven by line-scattering of the star’s continuum luminosity, and through transient eruptions identified as Luminous Blue Variables (LBVs). This paper compares and contrasts the processes involved in steady vs. eruptive mass loss, with an emphasis on their dependence on the star’s proximity to the classical Eddington limit. For winds, I examine the role of the iron opacity bump in initiating a quasi-continuum-driven outflow, which can induce atmospheric turbulence in O-stars, an envelope inflation cycle in LBVs, or enhanced wind mass loss in WR stars. In contrast, the giant eruptions of eruptive LBVs like $\eta$ Carinae require a sudden addition of energy to the stellar envelope, like that which can occur from stellar mergers. The positive net energy imparted to a substantial fraction (>10%) of the stellar mass leads to sudden ejection that closely follows an analytic exponential similarity solution. Moreover, the rapid rotation and enhanced luminosity of the post-merger star drive a super-Eddington wind. Due to equatorial gravity darkening, this wind is stronger over the poles, sculpting a bipolar structure in the ejected mass, consistent with observations of $\eta$ Carinae’s Homunculus nebula. Full article

CR is a cornerstone of secondary prevention for cardiovascular disease, offering well-established benefits across mortality, hospital readmission, functional capacity, and quality of life. Despite Class I guideline endorsements and decades of supporting evidence, CR remains vastly underutilized, particularly among women, racial and ethnic minorities, older adults, and individuals in low-resource settings. This review synthesizes the current evidence base for CR, with emphasis on disease-specific benefits across different cardiovascular diseases, and highlights recent data on its role in expanding populations, including patients with HFpEF, older adults, patients with advanced heart failure, and those undergoing transcatheter interventions. We also examine persistent barriers to CR access and participation, including system-level and referral limitations, as well as patient-level disparities by age, sex, race and ethnicity, and socioeconomic status. Building on this, we explore innovative delivery models and recent policy initiatives such as hybrid programs and reimbursement reform, all designed to expand access, promote equity, and modernize CR delivery. The findings underscore the need for continued investment, advocacy, and innovation to ensure equitable access to CR and its life-saving benefits across the full cardiovascular care continuum. Full article

Background: Atrial fibrosis is a key contributor to atrial cardiomyopathy and can be assessed invasively using mean left atrial voltage (MLAV) from electroanatomical mapping. However, the invasive nature of this procedure limits its clinical applicability. Machine learning (ML), particularly regression tree-based models, may offer a non-invasive approach for predicting MLAV using clinical and echocardiographic data, improving non-invasive atrial fibrosis characterisation beyond current dichotomous classifications. Methods: We prospectively included and followed 113 patients with paroxysmal or persistent atrial fibrillation (AF) undergoing pulmonary vein isolation (PVI) with ultra-high-density voltage mapping (uHDvM), from whom MLAV was estimated. Standardised two-dimensional transthoracic echocardiography was performed before ablation, and clinical and echocardiographic variables were analysed. A regression tree model was constructed using the Classification and Regression Trees—CART-algorithm to identify key predictors of MLAV. Results: The regression tree model exhibited moderate predictive accuracy (R² = 0.63; 95% CI: 0.55–0.71; root mean squared error = 0.90; 95% CI: 0.82–0.98), with indexed minimum LA volume and passive emptying fraction emerging as the most influential variables. No significant differences in AF recurrence-free survival were found among MLAV tertiles or model-based generated groups (log-rank p = 0.319 and p = 0.126, respectively). Conclusions: We present a novel ML-based regression tree model for non-invasive prediction of MLAV, identifying minimum LA volume and passive emptying fraction as the most significant predictors. This model offers an accessible, non-invasive tool for refining atrial cardiomyopathy characterisation by reflecting the fibrotic substrate as a continuum, a crucial advancement over existing dichotomous approaches to guide tailored therapeutic strategies. Full article

Fiber-reinforced and laminated composite materials, widely used in engineering applications, may develop periodic curvature during manufacturing due to technological requirements. Given such curvatures in widely used composites, static and dynamic analyses of plates and shells under loads, along with related stability issues, have been extensively investigated. However, studies focusing specifically on the static analysis of such materials remain limited. Composite materials with structural curvature exhibit complex mechanical behavior, making their analysis particularly challenging. Predicting their mechanical response is crucial in engineering. In response to this need, the present study conducts a static analysis of plates made of periodically curved composite materials using the Navier method. The plate equations were derived based on the Kirchhoff–Love plate theory within the framework of the Continuum Theory proposed by Akbarov and Guz’. Using the Navier method, deflection, stress, and moment distributions were obtained at every point of the plate. Numerical results were computed using MATLAB. After verifying the convergence and accuracy of the developed MATLAB code by comparing it with existing solutions for rectangular homogeneous isotropic and laminated composite plates, results were obtained for periodically curved plates. This study offers valuable insights that may guide future research, as it employs the Navier method to provide an analytical solution framework. This study contributes to the limited literature with a novel evaluation of the static analysis of composite plates with periodic curvature. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

Enhancing light absorption in two-dimensional (2D) materials, particularly few-layer structures, is critical for advancing optoelectronic devices such as light sources, photodetectors, and sensors. However, conventional absorption enhancement strategies often suffer from unstable resonant wavelengths and low-quality factors (Q-factors) due to the inherent weak light–matter interactions in 2D materials. To address these limitations, we propose an all-dielectric metasurface graphene-perfect absorber based on toroidal dipole bound state in the continuum (TD-BIC) with an ultra-narrow bandwidth and stable resonant wavelength. The proposed structure achieves tunable absorption linewidths spanning three orders of magnitude (6 nm to 0.0076 nm) through critical coupling modulation. Furthermore, the operational wavelength can be flexibly extended to any near-infrared region by adjusting the grating width. This work establishes a novel paradigm for enhancing the absorption of 2D materials in photonic device applications. Full article

This study presents a quasi-static optimization framework for the pressure-based control of a multi-segment soft continuum manipulator. The proposed method circumvents traditional curvature and length-based modeling by directly identifying the quasi-static input–output relationship between actuator pressures and the 6-DoF end-effector pose. Experimental data were collected using a high-frequency electromagnetic tracking system under monotonic pressurization to minimize hysteresis effects. Transfer functions were identified for each coordinate–actuator pair using the System Identification Toolbox in MATLAB, and optimal actuator pressures were computed analytically by solving a constrained quadratic program via a manual active-set method. The resulting control strategy achieved sub-millimeter positioning error while minimizing the number of actuators engaged. The approach is computationally efficient, sensor-minimal, and fully implementable in open-loop settings. Despite certain limitations due to sensor nonlinearity and actuator hysteresis, the method provides a robust foundation for feedforward control and the real-time deployment of soft robots in quasi-static tasks. Full article

Urban vegetation shows significant spatial differences due to the combined effects of natural and human factors, yet fine-scale evolutionary patterns and their cross-scale feedback mechanisms remain limited. This study focuses on the Yangtze River Delta (YRD), the top economic area in China. By integrating data from multiple Landsat sensors, we built a high—resolution framework to track vegetation dynamics from 1990 to 2020. It generates annual 30-m Enhanced Vegetation Index (EVI) data and uses a new Vegetation Green—Brown Balance Index (VBI) to measure changes between greening and browning. We combined Mann-Kendall trend analysis with machine—learning based attribution analysis to look into vegetation changes across different city types and urban—rural gradients. Over 30 years, the YRD’s annual EVI increased by 0.015/10 a, with greening areas 3.07 times larger than browning. Spatially, urban centers show strong greening, while peri—urban areas experience remarkable browning. Vegetation changes showed a city-size effect: larger cities had higher browning proportions but stronger urban cores’ greening trends. Cluster analysis finds four main evolution types, showing imbalances in grey—green infrastructure allocation. Vegetation baseline in 1990 is the main factor driving the long-term trend of vegetation greenness, while socioeconomic and climate drivers have different impacts depending on city size and position on the urban—rural continuum. In areas with low urbanization levels, climate factors matter more than human factors. These multi-scale patterns challenge traditional urban greening ideas, highlighting the need for vegetation governance that adapts to specific spatial conditions and city—unique evolution paths. Full article

We develop a symmetry-based variational theory that shows the coarse-grained balance of work inflow to heat outflow in a driven, dissipative system relaxed to the golden ratio. Two order-2 Möbius transformations—a self-dual flip and a self-similar shift—generate a discrete non-abelian subgroup of $\mathrm{PGL}(2,\mathbb{Q}\left(\sqrt{5}\right))$. Requiring any smooth, strictly convex Lyapunov functional to be invariant under both maps enforces a single non-equilibrium fixed point: the golden mean. We confirm this result by (i) a gradient-flow partial-differential equation, (ii) a birth–death Markov chain whose continuum limit is Fokker–Planck, (iii) a Martin–Siggia–Rose field theory, and (iv) exact Ward identities that protect the fixed point against noise. Microscopic kinetics merely set the approach rate; three parameter-free invariants emerge: a $62\%:38\%$ split between entropy production and useful power, an RG-invariant diffusion coefficient linking relaxation time and correlation length ${\mathcal{D}}_{\alpha }={\xi }^z/\tau$, and a $\vartheta ={45}^{\circ }$ eigen-angle that maps to the golden logarithmic spiral. The same dual symmetry underlies scaling laws in rotating turbulence, plant phyllotaxis, cortical avalanches, quantum critical metals, and even de-Sitter cosmology, providing a falsifiable, unifying principle for pattern formation far from equilibrium. Full article

We present an analytical framework for describing light propagation in infinite waveguide arrays, incorporating a generalized long-range coupling to achieve a more realistic model. We demonstrate that the resulting solution can be expressed in terms of generalized Bessel-like functions. Additionally, by applying the concept of eigenstates, we borrow from quantum mechanics a basis given in terms of phase states that allows the analysis of the transition from the discrete to the continuum limit, obtaining a relationship between the field amplitudes and the Fourier series coefficients of a given function. We apply our findings to different coupling functions, providing new insights into the propagation dynamics of these systems. Full article

Striking belief distortions may accompany various disorders of awareness that are predominantly associated with right hemispheric cerebral dysfunction. Distortions may range on a continuum of pathological severity, from the unawareness of paralysis in anosognosia for hemiplegia, to a more startling disturbance in denial of paralysis where belief may starkly conflict with reality. The patients’ beliefs about their limitations typically represent attempts to make sense of limitations or to impart meaning to incongruous facts. These beliefs are often couched in recollections from past memories or previous experience, and are hard to modify even given new information. Various explanations of unawareness have been suggested, including sensory, cognitive, monitoring and feedback operations, feedforward mechanisms, disconnection theories, and hemispheric asymmetry hypotheses, along with psychological denial, to account for the curious lack of awareness in anosognosia and other awareness disorders. This paper addresses these varying explanations of the puzzling beliefs regarding hemiparesis in anosognosia. Furthermore, using the multi-dimensional nature of unawareness in anosognosia as a model, some startling belief distortions in other right-hemisphere associated clinical syndromes are also explored. Other neurobehavioral disturbances, though perhaps less common, reflect marked psychopathological distortions. Startling disorders of belief are notable in somatic illusions, non-recognition or delusional misattribution of limb ownership (asomatognosia, somatoparaphrenia), or delusional identity (Capgras syndrome) and misidentification phenomena. Difficulty in updating beliefs as a source of unawareness in anosognosia and other awareness disorders has been proposed. Processes of belief development are considered to be patterns of thought, memories, and experience, which coalesce in a sense of the bodily and personal self. A common consequence of such disorders seems to be an altered representation of the self, self-parts, or the external world. Astonishing nonveridical beliefs about the body, about space, or about the self, continue to invite exploration and to stimulate fascination. Full article